# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2019-present, Facebook, Inc and the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import itertools
import math
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from ...utils.env import CONFIG_NAME
from .. import PretrainedModel, register_base_model
from ..activations import ACT2FN
from .configuration import (
XLM_PRETRAINED_INIT_CONFIGURATION,
XLM_PRETRAINED_RESOURCE_FILES_MAP,
XLMConfig,
)
__all__ = [
"XLMModel",
"XLMPretrainedModel",
"XLMWithLMHeadModel",
"XLMForSequenceClassification",
"XLMForTokenClassification",
"XLMForQuestionAnsweringSimple",
"XLMForMultipleChoice",
]
INF = 1e4
class SinusoidalPositionalEmbedding(nn.Embedding):
def __init__(self, num_embeddings, embedding_dim):
super().__init__(num_embeddings, embedding_dim)
self.weight = self._init_weight(self.weight)
@staticmethod
def _init_weight(out):
n_pos, dim = paddle.shape(out)
out.stop_gradient = True
position_ids = paddle.arange(0, n_pos, dtype=out.dtype).unsqueeze(1)
indices = paddle.arange(0, dim // 2, dtype=out.dtype).unsqueeze(0)
indices = 10000.0 ** (-2 * indices / dim)
embeddings = paddle.matmul(position_ids, indices)
out[:, 0::2] = paddle.sin(embeddings)
out[:, 1::2] = paddle.cos(embeddings)
return out
@paddle.no_grad()
def forward(self, position_ids):
return super().forward(position_ids)
def get_masks(seqlen, lengths, causal, padding_mask=None):
"""
Generate hidden states mask, and optionally an attention mask.
"""
alen = paddle.arange(0, seqlen, dtype="int64")
if padding_mask is not None:
mask = padding_mask
else:
mask = alen < lengths[:, None]
# attention mask is the same as mask, or triangular inferior attention (causal)
bs = paddle.shape(lengths)[0]
if causal:
attn_mask = paddle.tile(alen[None, None, :], (bs, seqlen, 1)) <= alen[None, :, None]
else:
attn_mask = mask
return mask, attn_mask
class MultiHeadAttention(nn.Layer):
NEW_ID = itertools.count()
def __init__(self, n_heads, dim, config: XLMConfig):
super().__init__()
self.layer_id = next(MultiHeadAttention.NEW_ID)
self.dim = dim
self.n_heads = n_heads
assert self.dim % self.n_heads == 0
self.q_lin = nn.Linear(dim, dim)
self.k_lin = nn.Linear(dim, dim)
self.v_lin = nn.Linear(dim, dim)
self.out_lin = nn.Linear(dim, dim)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
self.dim_per_head = self.dim // self.n_heads
def shape(self, x):
"""projection"""
return x.reshape([0, 0, self.n_heads, self.dim_per_head]).transpose([0, 2, 1, 3])
def unshape(self, x):
"""compute context"""
return x.transpose([0, 2, 1, 3]).reshape([0, 0, self.n_heads * self.dim_per_head])
def forward(self, input, mask, kv=None, cache=None, output_attentions=False):
"""
Self-attention (if kv is None) or attention over source sentence (provided by kv).
"""
# Input is (bs, qlen, dim)
# Mask is (bs, klen) (non-causal) or (bs, klen, klen)
bs, qlen, dim = paddle.shape(input)
if kv is None:
klen = qlen if cache is None else cache["seqlen"] + qlen
else:
klen = paddle.shape(kv)[1]
mask_reshape = (bs, 1, qlen, klen) if mask.ndim == 3 else (bs, 1, 1, klen)
q = self.shape(self.q_lin(input)) # (bs, n_heads, qlen, dim_per_head)
if kv is None:
k = self.shape(self.k_lin(input)) # (bs, n_heads, qlen, dim_per_head)
v = self.shape(self.v_lin(input)) # (bs, n_heads, qlen, dim_per_head)
elif cache is None or self.layer_id not in cache:
k = v = kv
k = self.shape(self.k_lin(k)) # (bs, n_heads, qlen, dim_per_head)
v = self.shape(self.v_lin(v)) # (bs, n_heads, qlen, dim_per_head)
if cache is not None:
if self.layer_id in cache:
if kv is None:
k_, v_ = cache[self.layer_id]
k = paddle.concat([k_, k], axis=2) # (bs, n_heads, klen, dim_per_head)
v = paddle.concat([v_, v], axis=2) # (bs, n_heads, klen, dim_per_head)
else:
k, v = cache[self.layer_id]
cache[self.layer_id] = (k, v)
q = q / math.sqrt(self.dim_per_head) # (bs, n_heads, qlen, dim_per_head)
scores = paddle.matmul(q, k, transpose_y=True) # (bs, n_heads, qlen, klen)
mask = mask.reshape(mask_reshape) # (bs, n_heads, qlen, klen)
scores = scores + (mask.astype(scores.dtype) - 1) * INF
weights = F.softmax(scores, axis=-1) # (bs, n_heads, qlen, klen)
weights = self.dropout(weights) # (bs, n_heads, qlen, klen)
context = paddle.matmul(weights, v) # (bs, n_heads, qlen, dim_per_head)
context = self.unshape(context) # (bs, qlen, dim)
outputs = (self.out_lin(context),)
if output_attentions:
outputs = outputs + (weights,)
return outputs
class TransformerFFN(nn.Layer):
def __init__(self, in_dim, dim_hidden, out_dim, config: XLMConfig):
super().__init__()
self.lin1 = nn.Linear(in_dim, dim_hidden)
self.lin2 = nn.Linear(dim_hidden, out_dim)
self.dropout = nn.Dropout(config.dropout_prob)
self.act = ACT2FN[config.hidden_act]
def forward(self, x):
x = self.lin1(x)
x = self.act(x)
x = self.lin2(x)
x = self.dropout(x)
return x
[文档]class XLMPretrainedModel(PretrainedModel):
"""
An abstract class for pretrained XLM models. It provides XLM related
`model_config_file`, `resource_files_names`, `pretrained_resource_files_map`,
`pretrained_init_configuration`, `base_model_prefix` for downloading and
loading pretrained models.
See :class:`~paddlenlp.transformers.model_utils.PretrainedModel` for more details.
"""
pretrained_init_configuration = XLM_PRETRAINED_INIT_CONFIGURATION
resource_files_names = {"model_state": "model_state.pdparams"}
pretrained_resource_files_map = XLM_PRETRAINED_RESOURCE_FILES_MAP
model_config_file = CONFIG_NAME
config_class = XLMConfig
base_model_prefix = "xlm"
def _init_weights(self, layer):
"""Initialization hook"""
if isinstance(layer, nn.Embedding):
new_weight = paddle.normal(
mean=0.0,
std=self.embed_init_std if hasattr(self, "embed_init_std") else self.xlm.config["embed_init_std"],
shape=layer.weight.shape,
)
if layer._padding_idx is not None:
new_weight[layer._padding_idx] = paddle.zeros_like(new_weight[layer._padding_idx])
layer.weight.set_value(new_weight)
elif isinstance(layer, nn.Linear):
layer.weight.set_value(
paddle.normal(
mean=0.0,
std=self.init_std if hasattr(self, "init_std") else self.xlm.config["init_std"],
shape=layer.weight.shape,
)
)
if layer.bias is not None:
layer.bias.set_value(paddle.zeros_like(layer.bias))
elif isinstance(layer, nn.LayerNorm):
layer.bias.set_value(paddle.zeros_like(layer.bias))
layer.weight.set_value(paddle.full_like(layer.weight, 1.0))
[文档]@register_base_model
class XLMModel(XLMPretrainedModel):
"""
The bare XLM Model transformer outputting raw hidden-states.
This model inherits from :class:`~paddlenlp.transformers.model_utils.PretrainedModel`.
Refer to the superclass documentation for the generic methods.
This model is also a Paddle `paddle.nn.Layer <https://www.paddlepaddle.org.cn/documentation
/docs/en/api/paddle/fluid/dygraph/layers/Layer_en.html>`__ subclass. Use it as a regular Paddle Layer
and refer to the Paddle documentation for all matter related to general usage and behavior.
Args:
config (:class:`XLMConfig`):
An instance of :class:`XLMConfig`.
"""
def __init__(self, config: XLMConfig):
super().__init__(config)
self.causal = config.causal
self.num_hidden_layers = config.num_hidden_layers
self.pad_token_id = config.pad_token_id
self.hidden_size = config.hidden_size
self.embed_init_std = config.embed_init_std
self.init_std = config.init_std
self.use_lang_embeddings = config.use_lang_embeddings
self.n_langs = config.n_langs
if not config.is_encoder:
raise NotImplementedError("Currently XLM can only be used as an encoder")
assert (
config.hidden_size % config.num_attention_heads == 0
), "xlm model's hidden_size must be a multiple of num_attention_heads"
# embeddings
if config.use_sinusoidal_embeddings:
self.position_embeddings = SinusoidalPositionalEmbedding(
config.max_position_embeddings, config.hidden_size
)
else:
self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
if config.n_langs > 1 and config.use_lang_embeddings:
self.lang_embeddings = nn.Embedding(config.n_langs, config.hidden_size)
self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size)
self.layer_norm_emb = nn.LayerNorm(config.hidden_size, epsilon=config.layer_norm_eps)
self.attentions = nn.LayerList()
self.layer_norm1 = nn.LayerList()
self.ffns = nn.LayerList()
self.layer_norm2 = nn.LayerList()
self.dropout = nn.Dropout(config.hidden_dropout_prob)
for _ in range(self.num_hidden_layers):
self.attentions.append(MultiHeadAttention(config.num_attention_heads, config.hidden_size, config))
self.layer_norm1.append(nn.LayerNorm(config.hidden_size, epsilon=config.layer_norm_eps))
self.ffns.append(
TransformerFFN(
config.hidden_size,
config.hidden_size * 4,
config.hidden_size,
config,
)
)
self.layer_norm2.append(nn.LayerNorm(config.hidden_size, epsilon=config.layer_norm_eps))
self.register_buffer(
"position_ids",
paddle.arange(0, config.max_position_embeddings).reshape((1, -1)),
persistable=False,
)
[文档] def forward(
self,
input_ids=None,
langs=None,
attention_mask=None,
position_ids=None,
lengths=None,
cache=None,
output_attentions=False,
output_hidden_states=False,
):
r"""
The XLMModel forward method, overrides the `__call__()` special method.
Args:
input_ids (Tensor):
Indices of input sequence tokens in the vocabulary. They are
numerical representations of tokens that build the input sequence.
Its data type should be `int64` and it has a shape of [batch_size, sequence_length].
langs (Tensor, optional):
A parallel sequence of tokens to be used to indicate the language of each token in the input. Indices are
languages ids which can be obtained from the language names by using two conversion mappings provided in
the configuration of the model (only provided for multilingual models). More precisely, the *language name
to language id* mapping is in `model.config['lang2id']` (which is a dictionary string to int).
Shape as [batch_size, sequence_length] and dtype as int64. Defaults to `None`.
attention_mask (Tensor, optional):
Mask used in multi-head attention to avoid performing attention on to some
unwanted positions, usually the paddings or the subsequent positions.
Its data type can be int, float and bool.
When the data type is bool, the `masked` tokens have `False` values and the others
have `True` values.
When the data type is int, the `masked` tokens have `0` values and the others have `1` values.
When the data type is float, the `masked` tokens have `0.0` values and the others have `1.0` values.
It is a tensor with shape broadcasted to `[batch_size, num_attention_heads, sequence_length, sequence_length]`.
Defaults to `None`, which means nothing needed to be prevented attention to.
position_ids (Tensor, optional):
Indices of positions of each input sequence tokens in the position embeddings. Selected
in the range `[0, max_position_embeddings - 1]`.
Shape as [batch_size, sequence_length] and dtype as int64. Defaults to `None`.
lengths (Tensor, optional):
Length of each sentence that can be used to avoid performing attention on padding token indices. You can
also use *attention_mask* for the same result (see above), kept here for compatibility. Indices selected in
`[0, ..., sequence_length]`.
Shape as [batch_size] and dtype as int64. Defaults to `None`.
cache (Tuple[Tuple[Tensor]], optional):
Contains pre-computed hidden-states (key and values in the attention blocks)
as computed by the model. Can be used to speed up sequential decoding.
The `input_ids` which have their past given to this model should not be
passed as input ids as they have already been computed.
Defaults to `None`.
output_attentions (bool, optional):
Whether or not to return the attentions tensors of all attention layers.
Defaults to `False`.
output_hidden_states (bool, optional):
Whether or not to return the output of all hidden layers.
Defaults to `False`.
Returns:
tuple: Returns tuple (`last_hidden_state`, `hidden_states`, `attentions`)
With the fields:
- `last_hidden_state` (Tensor):
Sequence of hidden-states at the last layer of the model.
It's data type should be float32 and its shape is [batch_size, sequence_length, hidden_size].
- `hidden_states` (tuple(Tensor), optional):
returned when `output_hidden_states=True` is passed.
Tuple of `Tensor` (one for the output of the embeddings + one for the output of
each layer). Each Tensor has a data type of float32 and its shape is
[batch_size, sequence_length, hidden_size].
- `attentions` (tuple(Tensor), optional):
returned when `output_attentions=True` is passed.
Tuple of `Tensor` (one for each layer) of shape. Each Tensor has a data type of
float32 and its shape is [batch_size, num_heads, sequence_length, sequence_length].
Example:
.. code-block::
import paddle
from paddlenlp.transformers import XLMModel, XLMTokenizer
tokenizer = XLMTokenizer.from_pretrained("xlm-mlm-tlm-xnli15-1024")
model = XLMModel.from_pretrained("xlm-mlm-tlm-xnli15-1024")
inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!", lang="en")
inputs = {k:paddle.to_tensor([v], dtype="int64") for (k, v) in inputs.items()}
inputs["langs"] = paddle.ones_like(inputs["input_ids"]) * tokenizer.lang2id["en"]
last_hidden_state = model(**inputs)[0]
"""
bs, seqlen = paddle.shape(input_ids)
if lengths is None:
if input_ids is not None:
lengths = (input_ids != self.pad_token_id).sum(axis=1).astype("int64")
else:
lengths = paddle.to_tensor([seqlen] * bs, dtype="int64")
# generate masks
mask, attn_mask = get_masks(seqlen, lengths, self.causal, padding_mask=attention_mask)
# position_ids
if position_ids is None:
position_ids = self.position_ids[:, :seqlen]
# do not recompute cached elements
if cache is not None and input_ids is not None:
_seqlen = seqlen - cache["seqlen"]
input_ids = input_ids[:, -_seqlen:]
position_ids = position_ids[:, -_seqlen:]
if langs is not None:
langs = langs[:, -_seqlen:]
mask = mask[:, -_seqlen:]
attn_mask = attn_mask[:, -_seqlen:]
# embeddings
tensor = self.embeddings(input_ids) + self.position_embeddings(position_ids)
if langs is not None and self.use_lang_embeddings and self.n_langs > 1:
tensor = tensor + self.lang_embeddings(langs)
tensor = self.layer_norm_emb(tensor)
tensor = self.dropout(tensor)
tensor = tensor * mask.unsqueeze(-1).astype(tensor.dtype)
# transformer layers
hidden_states = () if output_hidden_states else None
attentions = () if output_attentions else None
for i in range(self.num_hidden_layers):
if output_hidden_states:
hidden_states = hidden_states + (tensor,)
# self attention
attn_outputs = self.attentions[i](
tensor,
attn_mask,
cache=cache,
output_attentions=output_attentions,
)
attn = attn_outputs[0]
if output_attentions:
attentions = attentions + (attn_outputs[1],)
attn = self.dropout(attn)
tensor = tensor + attn
tensor = self.layer_norm1[i](tensor)
# FFN
tensor = tensor + self.ffns[i](tensor)
tensor = self.layer_norm2[i](tensor)
tensor = tensor * mask.unsqueeze(-1).astype(tensor.dtype)
# Add last hidden state
if output_hidden_states:
hidden_states = hidden_states + (tensor,)
# update cache length
if cache is not None:
cache["seqlen"] += paddle.shape(tensor)[1]
return tuple(v for v in [tensor, hidden_states, attentions] if v is not None)
class XLMPredLayer(nn.Layer):
"""
Prediction layer with cross_entropy.
"""
def __init__(
self,
config: XLMConfig,
embedding_weights=None,
):
super().__init__()
self.vocab_size = config.vocab_size
if embedding_weights is None:
self.proj = nn.Linear(config.hidden_size, config.vocab_size)
else:
self.bias = self.create_parameter(shape=[config.vocab_size], is_bias=True)
self.proj = lambda x: paddle.matmul(x, embedding_weights, transpose_y=True) + self.bias
def forward(self, x, y=None):
"""Compute the loss, and optionally the scores."""
outputs = ()
scores = self.proj(x)
outputs = (scores,) + outputs
if y is not None:
loss = F.cross_entropy(scores.reshape([-1, self.vocab_size]), y.flatten(), reduction="mean")
outputs = (loss,) + outputs
return outputs
[文档]class XLMWithLMHeadModel(XLMPretrainedModel):
"""
The XLM Model transformer with a masked language modeling head on top (linear
layer with weights tied to the input embeddings).
Args:
config (:class:`XLMConfig`):
An instance of :class:`XLMConfig`.
"""
def __init__(self, config: XLMConfig):
super().__init__(config)
self.xlm = XLMModel(config)
self.pred_layer = XLMPredLayer(
config,
embedding_weights=self.xlm.embeddings.weight,
)
[文档] def forward(
self, input_ids=None, langs=None, attention_mask=None, position_ids=None, lengths=None, cache=None, labels=None
):
r"""
The XLMWithLMHeadModel forward method, overrides the __call__() special method.
Args:
input_ids (Tensor):
See :class:`XLMModel`.
langs (Tensor, optional):
See :class:`XLMModel`.
attention_mask (Tensor, optional):
See :class:`XLMModel`.
position_ids (Tensor, optional):
See :class:`XLMModel`.
lengths (Tensor, optional):
See :class:`XLMModel`.
cache (Dict[str, Tensor], optional):
See :class:`XLMModel`.
labels (Tensor, optional):
The Labels for computing the masked language modeling loss. Indices are selected in
`[-100, 0, ..., vocab_size-1]` All labels set to `-100` are ignored (masked), the loss is
only computed for labels in `[0, ..., vocab_size-1]`
Shape as [batch_size, sequence_length] and dtype as int64. Defaults to `None`.
Returns:
tuple: Returns tuple `(loss, logits)`.
With the fields:
- `loss` (Tensor):
returned when `labels` is provided.
Language modeling loss (for next-token prediction).
It's data type should be float32 and its shape is [1,].
- `logits` (Tensor):
Prediction scores of the language modeling head (scores for each vocabulary
token before SoftMax).
It's data type should be float32 and
its shape is [batch_size, sequence_length, vocab_size].
Example:
.. code-block::
import paddle
from paddlenlp.transformers import XLMWithLMHeadModel, XLMTokenizer
tokenizer = XLMTokenizer.from_pretrained('xlm-mlm-tlm-xnli15-1024')
model = XLMWithLMHeadModel.from_pretrained('xlm-mlm-tlm-xnli15-1024')
inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!", lang="en")
inputs = {k:paddle.to_tensor([v], dtype="int64") for (k, v) in inputs.items()}
inputs["langs"] = paddle.ones_like(inputs["input_ids"]) * tokenizer.lang2id["en"]
inputs["labels"] = inputs["input_ids"]
loss, logits = model(**inputs)
"""
xlm_outputs = self.xlm(
input_ids,
langs=langs,
attention_mask=attention_mask,
position_ids=position_ids,
lengths=lengths,
cache=cache,
)
output = xlm_outputs[0]
outputs = self.pred_layer(output, labels)
return outputs + xlm_outputs[1:]
[文档]class XLMForSequenceClassification(XLMPretrainedModel):
"""
The XLMModel with a sequence classification head on top (linear layer).
`XLMForSequenceClassification` uses the first token in order to do the classification.
Args:
config (:class:`XLMConfig`):
An instance of :class:`XLMConfig`.
"""
def __init__(self, config: XLMConfig):
super().__init__(config)
self.num_classes = config.num_classes
self.xlm = XLMModel(config)
dropout_prob = config.dropout if config.dropout is not None else config.hidden_dropout_prob
self.dropout = nn.Dropout(dropout_prob)
self.classifier = nn.Linear(config.hidden_size, config.num_classes)
[文档] def forward(self, input_ids=None, langs=None, attention_mask=None, position_ids=None, lengths=None):
r"""
The XLMForSequenceClassification forward method, overrides the __call__() special method.
Args:
input_ids (Tensor):
See :class:`XLMModel`.
langs (Tensor, optional):
See :class:`XLMModel`.
attention_mask (Tensor, optional):
See :class:`XLMModel`.
position_ids (Tensor, optional):
See :class:`XLMModel`.
lengths (Tensor, optional):
See :class:`XLMModel`.
Returns:
logits (Tensor):
A tensor of the input text classification logits.
Shape as `[batch_size, num_classes]` and dtype as float32.
Example:
.. code-block::
import paddle
from paddlenlp.transformers import XLMForSequenceClassification, XLMTokenizer
tokenizer = XLMTokenizer.from_pretrained("xlm-mlm-tlm-xnli15-1024")
model = XLMForSequenceClassification.from_pretrained("xlm-mlm-tlm-xnli15-1024", num_classes=2)
inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!", lang="en")
inputs = {k:paddle.to_tensor([v], dtype="int64") for (k, v) in inputs.items()}
inputs["langs"] = paddle.ones_like(inputs["input_ids"]) * tokenizer.lang2id["en"]
logits = model(**inputs)
"""
sequence_output = self.xlm(
input_ids, langs=langs, attention_mask=attention_mask, position_ids=position_ids, lengths=lengths
)[0]
sequence_output = self.dropout(sequence_output)
pooled_output = sequence_output[:, 0]
logits = self.classifier(pooled_output)
return logits
[文档]class XLMForTokenClassification(XLMPretrainedModel):
"""
XLMModel with a linear layer on top of the hidden-states output layer,
designed for token classification tasks like NER tasks.
Args:
config (:class:`XLMConfig`):
An instance of :class:`XLMConfig`.
"""
def __init__(self, config: XLMConfig):
super(XLMForTokenClassification, self).__init__(config)
self.num_classes = config.num_classes
self.xlm = XLMModel(config) # allow xlm to be config
self.dropout = nn.Dropout(config.dropout if config.dropout is not None else config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, config.num_classes)
[文档] def forward(self, input_ids=None, langs=None, attention_mask=None, position_ids=None, lengths=None):
r"""
The XLMForTokenClassification forward method, overrides the __call__() special method.
Args:
input_ids (Tensor):
See :class:`XLMModel`.
langs (Tensor, optional):
See :class:`XLMModel`.
attention_mask (Tensor, optional):
See :class:`XLMModel`.
position_ids (Tensor, optional):
See :class:`XLMModel`.
lengths (Tensor, optional):
See :class:`XLMModel`.
Returns:
logits (Tensor):
A tensor of the input token classification logits.
Shape as `[batch_size, sequence_length, num_classes]` and dtype as `float32`.
Example:
.. code-block::
import paddle
from paddlenlp.transformers import XLMForTokenClassification, XLMTokenizer
tokenizer = XLMTokenizer.from_pretrained("xlm-mlm-tlm-xnli15-1024")
model = XLMForTokenClassification.from_pretrained("xlm-mlm-tlm-xnli15-1024", num_classes=2)
inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!", lang="en")
inputs = {k:paddle.to_tensor([v], dtype="int64") for (k, v) in inputs.items()}
inputs["langs"] = paddle.ones_like(inputs["input_ids"]) * tokenizer.lang2id["en"]
logits = model(**inputs)
"""
sequence_output = self.xlm(
input_ids, langs=langs, attention_mask=attention_mask, position_ids=position_ids, lengths=lengths
)[0]
sequence_output = self.dropout(sequence_output)
logits = self.classifier(sequence_output)
return logits
[文档]class XLMForQuestionAnsweringSimple(XLMPretrainedModel):
"""
XLMModel with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
Args:
config (:class:`XLMConfig`):
An instance of :class:`XLMConfig`.
"""
def __init__(self, config: XLMConfig):
super(XLMForQuestionAnsweringSimple, self).__init__(config)
self.xlm = XLMModel(config) # allow xlm to be config
self.classifier = nn.Linear(config.hidden_size, 2)
[文档] def forward(self, input_ids=None, langs=None, attention_mask=None, position_ids=None, lengths=None):
r"""
The XLMForQuestionAnswering forward method, overrides the __call__() special method.
Args:
input_ids (Tensor):
See :class:`XLMModel`.
langs (Tensor, optional):
See :class:`XLMModel`.
attention_mask (Tensor, optional):
See :class:`XLMModel`.
position_ids (Tensor, optional):
See :class:`XLMModel`.
lengths (Tensor, optional):
See :class:`XLMModel`.
Returns:
tuple: Returns tuple (`start_logits`, `end_logits`).
With the fields:
- `start_logits` (Tensor):
A tensor of the input token classification logits, indicates the start position of the labelled span.
Its data type should be float32 and its shape is [batch_size, sequence_length].
- `end_logits` (Tensor):
A tensor of the input token classification logits, indicates the end position of the labelled span.
Its data type should be float32 and its shape is [batch_size, sequence_length].
Example:
.. code-block::
import paddle
from paddlenlp.transformers import XLMForQuestionAnswering, XLMTokenizer
tokenizer = XLMTokenizer.from_pretrained("xlm-mlm-tlm-xnli15-1024")
model = XLMForQuestionAnswering.from_pretrained("xlm-mlm-tlm-xnli15-1024", num_classes=2)
inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!", lang="en")
inputs = {k:paddle.to_tensor([v], dtype="int64") for (k, v) in inputs.items()}
inputs["langs"] = paddle.ones_like(inputs["input_ids"]) * tokenizer.lang2id["en"]
outputs = model(**inputs)
start_logits = outputs[0]
end_logits = outputs[1]
"""
sequence_output = self.xlm(
input_ids, langs=langs, attention_mask=attention_mask, position_ids=position_ids, lengths=lengths
)[0]
logits = self.classifier(sequence_output)
start_logits, end_logits = paddle.unstack(x=logits, axis=-1)
return start_logits, end_logits
